{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],alpha=0.8, c=cmap(idx),marker=markers[idx], label=cl)\n",
    "    # highlight test samples\n",
    "    if test_idx:\n",
    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "        plt.scatter(X_test[:, 0], X_test[:, 1], c='', alpha=1.0, linewidth=1, marker='o', s=55, label='test set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aS+B/NimXMlNzsMZ1m+1iezzOS5kUSgCfAu4DlhhjLrbWvpu7IUm2Omo3nnrF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\nrQ1UxwQ1c3AvCBMrgzCGeADF16AYPz7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W+JoxZm/Cp0uJPnfzdg7HFlqarKnvQSCktvnW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\njrtSE2STJElFNkrDpDgtq9N8S9JoqDIdtxMsSFJtNkpt581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      "text/plain": [
       "<matplotlib.figure.Figure at 0x212e74eb438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "iris = datasets.load_iris() # 由于Iris是很有名的数据集，scikit-learn已经原生自带了。\n",
    "X = iris.data[:, [1, 2]]\n",
    "y = iris.target # 标签已经转换成0，1，2了\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) # 为了看模型在没有见过数据集上的表现，随机拿出数据集中30%的部分做测试\n",
    "\n",
    "# 为了追求机器学习和最优化算法的最佳性能，我们将特征缩放\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train) # 估算每个特征的平均值和标准差\n",
    "sc.mean_ # 查看特征的平均值，由于Iris我们只用了两个特征，所以结果是array([ 3.82857143,  1.22666667])\n",
    "sc.scale_ # 查看特征的标准差，这个结果是array([ 1.79595918,  0.77769705])\n",
    "X_train_std = sc.transform(X_train)\n",
    "# 注意：这里我们要用同样的参数来标准化测试集，使得测试集和训练集之间有可比性\n",
    "X_test_std = sc.transform(X_test)\n",
    "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
    "y_combined = np.hstack((y_train, y_test))\n",
    "\n",
    "# 导入SVC\n",
    "from sklearn.svm import SVC\n",
    "svm1 = SVC(kernel='linear', C=0.1, random_state=0) # 用线性核\n",
    "svm1.fit(X_train_std, y_train)\n",
    "\n",
    "svm2 = SVC(kernel='linear', C=10, random_state=0) # 用线性核\n",
    "svm2.fit(X_train_std, y_train)\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "#ax2 = fig.add_subplot(1,2,2)\n",
    "\n",
    "plot_decision_regions(X_combined_std, y_combined, classifier=svm1)\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.title('C = 0.1')\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "plot_decision_regions(X_combined_std, y_combined, classifier=svm2)\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.title('C = 10')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K8nuWY61lxBkj2PbKNp6sfJKp505V9wYREckp5ZJIcVJFSSLp+vnXUzm/kqULlrabXUhE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\nTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x212e8959a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "svm1 = SVC(kernel='rbf', random_state=0, gamma=0.1, C=1.0) # 令gamma参数中的x分别等于0.1和10\n",
    "svm1.fit(X_train_std, y_train) \n",
    "\n",
    "svm2 = SVC(kernel='rbf', random_state=0, gamma=10, C=1.0) \n",
    "svm2.fit(X_train_std, y_train) \n",
    "\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "\n",
    "\n",
    "plot_decision_regions(X_combined_std, y_combined, classifier=svm1)\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.title('gamma = 0.1')\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "plot_decision_regions(X_combined_std, y_combined, classifier=svm2)\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.title('gamma = 10')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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